class_docstr: This class decodes each item in a batch by resizing each image
  window and save as a new image volume. Multiple output image can be
  proposed and csv output can be performed as well
class_docstr_tok: ["This","class","decodes","each","item","in","a","batch","by","resizing","each","image","window","and","save","as","a","new","image","volume",".","Multiple","output","image","can","be","proposed","and","csv","output","can","be","performed","as","well"]
code: def __init__(
      self,
      image_reader,
      name="image",
      output_path=os.path.join(".", "output"),
      window_border=(),
      interp_order=0,
      postfix="niftynet_out",
  ):
      ImageWindowsAggregator.__init__(
          self, image_reader=image_reader, output_path=output_path
      )
      self.name = name
      self.image_out = None
      self.csv_out = None
      self.window_border = window_border
      self.output_interp_order = interp_order
      self.postfix = postfix
      self.current_out = {}
  
file_path: NiftyNet/niftynet/engine/windows_aggregator_resize.py
imports: from __future__ import absolute_import, division, print_function
  import os
  from collections import OrderedDict
  import numpy
  import pandas
  import niftynet.io.misc_io
  from niftynet.engine.sampler_resize_v2 import zoom_3d
  from niftynet.engine.windows_aggregator_base import ImageWindowsAggregator
  from niftynet.layer.discrete_label_normalisation import DiscreteLabelNormalisationLayer
  from niftynet.layer.pad import PadLayer
label_desc: Use image node_id 1 to create a new ResizeSamplesAggregator object from inherited base classes: ImageWindowsAggregator with example: obj = ResizeSamplesAggregator(image_reader, name, output_path, window_border, interp_order, postfix)